Core Lab Members:

Mike completed his graduate work at Queen's University in Canada under the supervision of Doug Mewhort, and then spent two years as a postdoctoral research fellow at the Institute of Cognitive Science, University of Colorado with Walter Kintsch and Tom Landauer. In 2006, he moved to Indiana University where he currently holds the William and Katherine Estes Endowed Chair in the Department of Psychological and Brain Sciences.

Mike's research can be broadly defined as computational approaches to human language and memory, and artificial intelligent systems.

Jack completed his undergraduate work at the University of Nebraska. His primary research interests are in developing computational models to understand how humans integrate perceptual and linguistic information during learning. He is also interested in how humans search memory in strategic tasks, and the development of systems to assist in memory encoding and retrieval.

Willa completed her undergraduate degrees in Cognitive Science and Computing Science at IU, and began in the PhD program in 2018. Willa's general interests are in developing computational models of human memory search and retention, with particular emphasis on bringing theoretical insights from cognitive models to applied problems in artificial intelligence.

Keiland is pursuing his undergraduate degree in Cognitive Science and Neuroscience at IU with a minor in Computing Science. He is generally interested in how memory emerges from circuits in the brain, and in artificial intelligence approaches to understand and compliment human intelligence. In the lab he is working on modeling projects to better understand how deep learning models may be integrated with cognitive process models to simulate different kinds of semantic relations dependent on task context. ​

Tom is an Adjunct Professor and in the Department of Psychological & Brain Sciences and a Research Scientist in David Pisoni'sSpeech Research Laboratory (SRL). He has been leading projects joining SRL and CCL in understanding the statistical properties of the mental lexicon, reverse engineering semantic structure from converging behavioral data, and understanding the relationship between auditory and visual word recognition.

Lauren Jones, Experiment Tester

Lauren is interested in statistical language learning and perceptual integration from a first-person perspective. We are attempting to reverse-engineer her to make machines that are as smart, but hopefully listen a tad better. To date, she is the most biologically plausible neural network Dr. Jones has ever programmed. How exactly she works is still somewhat of a mystery though.